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Rescooped by Lahoucine Hanich from Digital Cartography
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Google Earth A to Z: Street View, Sharing, and Smoots | Google Earth Blog

Google Earth A to Z: Street View, Sharing, and Smoots | Google Earth Blog | earth sciences | Scoop.it

Google first added Street View imagery to Google Earth in 2008, and gave it a substantial upgrade in 2010 with the release of Google Earth 6. Rather than having a separate layer for Street View, they added a "pegman" icon to the controls on the right side of the screen, very similar to how it works in Google Maps. Here's a quick video on how to use it::.....


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Rescooped by Lahoucine Hanich from Remote Sensing News
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Supervised Classification with ArcGIS

Supervised Classification with ArcGIS | earth sciences | Scoop.it
This post describes how to use the image classification tools in Image Analysis for Supervised Classification.

 

What You Will Need and Other Assumptions
• A satellite image that has been properly prepared for input to an image classification (e.g.,atmospheric correction, terrain correction, etc.)
• Aerial photographs and other ancillary data for the same area, as well as good local knowledge of the terrain, vegetation and soils for the area
• The Supervised Classification tools in Image Analysis are very limited as compared to those available in ERDAS Imagine....


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Pollutants linked to ‘fracking’ found in Wyoming groundwater - The Hill's E2-Wire

Pollutants linked to ‘fracking’ found in Wyoming groundwater - The Hill's E2-Wire | earth sciences | Scoop.it
Retesting of groundwater in a Wyoming town that magnified debates about environmental hazards regarding natural gas drilling revealed chemicals previously linked to hydraulic fracturing. A U.
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Rescooped by Lahoucine Hanich from R and Geostatistics
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A multivariate approach for anomaly separation of potentially toxic trace elements in urban and peri-urban soils

Purpose
Geogenic soil enrichment and anthropogenic pollution by potentially toxic trace elements (PTEs) are two processes acting together. Although it is often difficult, it is necessary to separate the two processes for risk assessment and understanding the environmental implications. The aim of this study was to analyse the soil concentrations of various PTEs in a southern Italy area in order to: (1) determine their different correlation structure to isolate sources of variation acting at different spatial scales and (2) to define potential anomalies based on the correlation structure.


Materials and methods
In the urban and peri-urban area of Cosenza-Rende, 149 topsoil samples were collected (0.10 m) and analysed for different elements by X-ray fluorescence spectrometry. Principal component analysis and factorial kriging analysis were used to map the spatial distribution of PTEs in topsoil and to identify the main factors influencing their spatial variability.


Results and discussion
Two groups of PTEs were identified: the first group included As, Pb and Zn; and the second one Al, Co, Cr, Fe, La, Nb, Ni, Ti and V. The first group was related to anthropogenic causes, while the second one was more related to parent rock composition. The regionalized factors at different scales of variability allowed to aggregate and summarize the joint variability in the PTEs and consider the probable causes of soil pollution.


Conclusions
The study allowed analysing and quantifying the sources (environmental or anthropogenic) of variation of PTEs acting at different spatial scale and defining the spatial anomalies based on the correlation structure associated at the different spatial scales.


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